# New Preprint Shows Quantization Can Switch On Hidden Backdoors That Full-Precision Testing Never Sees

Backdoored translation models measured clean at 16-bit precision produced corrupted output in up to 85.02 percent of cases after routine post-training compression.

- Published: 2026-08-31T06:23:05.714Z
- Canonical: https://polylog.news/ai/2026-08-31/new-preprint-shows-quantization-can-switch-on-hidden-backdoo
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [arXiv cs.LG](https://arxiv.org/abs/2608.27512), [arXiv cs.LG](https://arxiv.org/abs/2608.27513)

A preprint posted to arXiv today challenges an assumption that most deployment pipelines rely on without stating it: that post-training quantization is a semantically neutral optimization. The authors argue that because quantization maps ma…

This story is for subscribers. Read it in full at https://polylog.news/ai/2026-08-31/new-preprint-shows-quantization-can-switch-on-hidden-backdoo (subscription information: https://polylog.news/pricing).